Trajectories of cardiac troponin in the decades before cardiovascular death: a longitudinal cohort study

Background High-sensitivity cardiac troponin testing is a promising tool for cardiovascular risk prediction, but whether serial testing can dynamically predict risk is uncertain. We evaluated the trajectory of cardiac troponin I in the years prior to a cardiovascular event in the general population, and determine whether serial measurements could track risk within individuals. Methods In the Whitehall II cohort, high-sensitivity cardiac troponin I concentrations were measured on three occasions over a 15-year period. Time trajectories of troponin were constructed in those who died from cardiovascular disease compared to those who survived or died from other causes during follow up and these were externally validated in the HUNT Study. A joint model that adjusts for cardiovascular risk factors was used to estimate risk of cardiovascular death using serial troponin measurements. Results In 7,293 individuals (mean 58 ± 7 years, 29.4% women) cardiovascular and non-cardiovascular death occurred in 281 (3.9%) and 914 (12.5%) individuals (median follow-up 21.4 years), respectively. Troponin concentrations increased in those dying from cardiovascular disease with a steeper trajectory compared to those surviving or dying from other causes in Whitehall and HUNT (Pinteraction < 0.05 for both). The joint model demonstrated an independent association between temporal evolution of troponin and risk of cardiovascular death (HR per doubling, 1.45, 95% CI,1.33–1.75). Conclusions Cardiac troponin I concentrations increased in those dying from cardiovascular disease compared to those surviving or dying from other causes over the preceding decades. Serial cardiac troponin testing in the general population has potential to track future cardiovascular risk. Supplementary Information The online version contains supplementary material available at 10.1186/s12916-023-02921-8.


Joint modelling
The principle of joint modelling To evaluate the association between a longitudinal marker (i.e., a measurement repeatedly monitored over time [cardiac troponin]), and the occurrence of an event over time (i.e. cardiovascular death), a joint model can be used. The joint model uses a separate regression model to describe the evolution of the marker over time and uses these estimated evolutions in a time-to-event relative risk model for the event of interest. A linear mixed-effects model is used to analyze the longitudinal marker over time, which results in an estimated level of the marker at each point in time, instead of assuming a constant level of the longitudinal marker between observed measurements. In the joint model, this estimated evolution is related to the event status (i.e., the estimated cardiac troponin I at the time of the event is used for the relative risk analysis, Figure 1A). For the joint modelling approach at least one measurement of a predictor variable is required per outcome and per individual and it does not matter which measurement is used (i.e., at baseline or at a later time point). Provided the above condition holds, the joint model works with all available data per outcome. Implicitly the missing data are imputed by the model under the missing not at random (MNAR) assumption, with specific missing data mechanism implied by joint models Apart from relating the level of the marker to the risk of the event, the joint modeling framework allows for extensions assessing additional associations. Perhaps it is not (only) the level of the marker that is related to the event but the fact that the marker is increasing rapidly at that moment. This would be of particular interest in situations where, for example, at a specific point in time two individuals show similar marker levels, but different rate of change of the marker.
The rate of change of a longitudinal marker (i.e., the slope of the marker at that moment in time), can be added to the joint model to analyze its relationship with the event of interest. The slope, as evaluated by the joint model, indicates whether and by how much a marker is increasing or decreasing at any moment ( Figure 1B), which differs from the absolute (or relative) rate of change that corresponds to the constant rate of change between two time points ( Figure 2). In our study, we observed a non-significant relationship with the slope of cardiac troponin the hazard of the event. The level of cardiac troponin remains significant, so we can conclude that the velocity of the concentration has no significant additional information on top of its absolute level at each point in time. Incorporating troponin rate of change together with absolute concentrations may give additional information on the risk of cardiovascular death, although absolute concentrations appear the driving factor for predicting cardiovascular death.
Apart from the level and slope of the longitudinal marker, multiple other features can easily be included in the joint model, for instance: area under the curve (if the cumulative burden of a marker has an effect on the event, Figure 1C). The estimated joint model can be used to make individualized predictions. Based on a set of repeated measurements of the marker and relevant baseline covariates, the model can make predictions on future levels of the marker, and, more interestingly, on the probabilities of a future event. With graphs, it can be shown directly how adding new marker information updates the event probability of an individual. This is demonstrated in Figure 3 in the manuscript for two individuals. Each individual has several plots, with an increasing number of cardiac troponin measurements. On the left part of each plot, the concentrations are plotted with the estimated trajectory through them. The right part of each plot shows the corresponding predicted event probabilities for that individual, which are equal to one minus the survival probabilities. The shaded grey area represents the 95% confidence interval.

Joint model versus Time-Dependent Cox model
The joint model offers several advantages over traditional approaches for analyzing time-toevent data, such as Cox regression with a time-dependent covariate. For instance, the timedependent cox model assumes that the level of a covariate stays constant in between two measurements when comparing marker levels of patients with and without event. In contrast, the joint model uses a separate regression model to describe the evolution of the marker over time and uses this estimated evolution in a time-to-event relative risk model for the event of interest. Additionally, the time-dependent cox model assumes that the availability of a measurement is not related to the event status. This indicates that the longitudinal marker needs to be an exogenous or external variable. However, cardiac troponin is a measurement taken from the individual and is, therefore, an endogenous or internal variable. The joint model is a more appropriate model compared to the time-dependent Cox model to analyze data with these features. Furthermore, as described above, an estimated joint model can be used to make individualized predictions on future levels of the marker and probabilities of a future event. The time-dependent cox model cannot be used to make survival predictions in a dynamic matter because the anticipated changes in future values of the time-varying covariate are not incorporated in calculating survival predictions for a single Cox model. In the primary analysis we have conducted joint modelling. For the external validation using HUNT, we were not able to conduct joint modelling, as we had a maximum number of two measurements per individual available. We attempted to fit our joint model to these data, but an accurate estimation of individual troponin trajectories is required to use this technique. Two measurements combined with a time gap of ten years between last measurement and end of follow-up precludes us to estimate the evolution of cardiac troponin accurately. We were therefore restricted to evaluate the relationship with serial cardiac troponin testing and cardiovascular death using Time-Dependent Cox regression analysis.   We determined the longitudinal cardiac troponin's predictive accuracy (i.e., an ability of cardiac troponin to discriminate between an individual who died due to cardiovascular disease within a given risk time window after the last measurement, and the individual who does not experience the event within the same risk time window) using the time-dependent area under the curve. For this purpose, we chose the first 16 years as the collection time period, and we assessed risk time windows at 2 and 5 years after collection time. We determined the predictive accuracy of the cardiac troponin's levels in univariable and multivariable adjusted non-competing risk models. * The model adjusted for known cardiovascular risk factors included age, sex, diabetes mellitus, total cholesterol, high-density lipoprotein, systolic blood pressure, smoking status and serial cardiac troponin measurements. Abbreviations: AUC, area under the curve; CVD, cardiovascular disease.

Fig. S3
Trajectories of cardiac troponin I with 95% confidence intervals before noncardiovascular death occurred or at end of follow-up. The red line refers to the average troponin trajectory of those individuals who died due to non-cardiovascular causes, and the blue line refers to the average troponin trajectory of those individuals who survived or died due to cardiovascular causes. Estimates are adjusted for sex and age.

Fig. S4
Trajectories of cardiac troponin I with 95% confidence intervals before cardiovascular death or non-cardiovascular death occurred or at end of follow-up. The red line refers to the average troponin trajectory of those individuals who died due to cardiovascular causes, the light blue line refers to the average troponin trajectory of those individuals who survived, and the dark blue line refers to the average troponin trajectory of those individuals who died due to non-cardiovascular causes. Estimates are adjusted for sex and age.

Fig. S5
Trajectories of cardiac troponin I with 95% confidence intervals before cardiac death occurred or at end of follow-up. The red line refers to the average troponin trajectory of those individuals who died due to cardiac causes, and the blue line refers to the average troponin trajectory of those individuals who survived or died due to non-cardiac causes.
Estimates are adjusted for sex and age.

Fig. S6 Trajectories of cardiac troponin I with 95% confidence intervals before non-fatal myocardial infarction, fatal myocardial infarction and no myocardial infarction event.
The red line refers to the average troponin trajectory of those individuals who experienced a non-fatal myocardial infarction, the dark blue line refers to the average troponin trajectory of those individuals who experienced a fatal myocardial infarction, and the light blue line refers to the average troponin trajectory of those individuals who survived without myocardial infarction or died from other causes. Estimates are adjusted for sex and age. MI=myocardial infarction.

Fig. S7
Trajectories of cardiac troponin I with 95% confidence intervals in individuals without baseline cardiac disease before cardiovascular death occurred or at end of followup. The red line refers to the average troponin trajectory of those individuals who died due to cardiovascular causes, and the blue line refers to the average troponin trajectory of those individuals who survived or died due to non-cardiovascular causes. Estimates are adjusted for sex and age.

Fig. S8 Trajectories of cardiac troponin I with 95% confidence intervals before cardiovascular death (Panel A) and death from any cause (Panel B) in HUNT.
In Panel A, the red line refers to the average troponin trajectory of those individuals who died due to cardiovascular causes, and the blue line refers to the average troponin trajectory of those individuals who survived or died due to non-cardiovascular causes. In Panel B, the red line refers to the average troponin trajectory of those individuals who died from any cause, and the blue line refers to the average troponin trajectory of those individuals who survived. Estimates are adjusted for sex and age.